Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A gene pattern mining algorithm using interchangeable gene sets for prokaryotes.

Meng Hu1, Kwangmin Choi, Wei Su

  • 1EECS, Case Western Reserve University, Cleveland, OH 44106 USA. meng.hu@case.edu

BMC Bioinformatics
|February 28, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cation-templated synthesis of a Fe<sub>4</sub>Co<sub>20</sub> cyanometallate cluster.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

Glass-like anomalies and unconventional thermoelectric transport in chimney ladder crystals.

Nature communications·2026
Same author

Exceptional Rare-Earth Half-Heusler Thermoelectrics With Sublattice Softening.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Interface Excitons in van der Waals Sandwich Heterostructures.

ACS nano·2026
Same author

Pressure-Induced Superconductivity in the Thermoelectric Semiconductor Mg<sub>3</sub>Sb<sub>2</sub>.

Journal of the American Chemical Society·2026
Same author

circTMEM230 Sponges miR-223-3p to Promote Endplate Chondrocyte Extracellular Matrix Synthesis and Attenuate Tension-Induced Disc Degeneration.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

This study introduces a new algorithm for discovering gene patterns across multiple prokaryote genomes, even without family classification. The method improves ortholog prediction accuracy compared to existing techniques.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Mining common gene patterns across genomes offers biological insights.
  • Existing methods struggle with multiple genomes, especially without gene family data.
  • Extending pairwise genome algorithms to multiple genomes is computationally challenging.

Purpose of the Study:

  • To develop a novel algorithm for mining gene patterns in multiple prokaryote genomes.
  • To address the challenge of mining gene patterns when family classification is unavailable.
  • To improve ortholog prediction using discovered gene patterns.

Main Methods:

  • Proposed a novel algorithm utilizing interchangeable sets for multi-genome gene pattern mining.
  • Extended data mining pattern techniques to handle situations lacking gene family classification.

Related Experiment Videos

  • Developed an ortholog prediction method based on the gene pattern mining algorithm.

    • Main Results:

    • Demonstrated that gene patterns capture significant biological information in newly sequenced genomes.
    • Achieved a 3% increase in recall for ortholog detection compared to the bi-directional best hit (BBH) method.
    • Maintained precision in ortholog detection while improving recall.

    Conclusions:

    • Discovered gene patterns can effectively identify orthologs.
    • Gene patterns aid in detecting genes involved in collaborative biological functions.